PROJECT SUMMARY/ABSTRACT The observation that potentially competing populations of tumor infiltrating myeloid and lymphocytic cells in undifferentiated pleomorphic sarcomas (UPS) and the balance between these cellular populations may belie treatment response to radiation therapy (RT) provides a clinical diagnostic and treatment dilemma. However this is also an opportunity to utilize multi-modal quantitative measures on the cell and tissue levels in order to construct computational models that characterize the functional metabolic states of the tumor microenvironments and potential therapeutic targeting strategies by modulating the myeloid vs lymphocytic cell populations. Dr. Neema Jamshidi hypothesizes that RT affects the metabolic state of myeloid tumor infiltrating cells, infiltrating T cells, as well as tumors and that hierarchical measurements (cellular omics with quantitative mpMRI) provide a means to classify different RT treatment responsive tumors across multiple spatial scales. This hypothesis will be addressed through the use of a systems approach for metabolic characterization of the multi-cellular interactions in the sarcoma microenvironment using multi-parametric magnetic resonance imaging (mpMRI) in conjunction with tissue-cleared imaging and multi-omics characterization of cells in order to construct spatially hierarchical models of metabolism building upon the constraint-based integrative modeling framework. A systematic approach will be employed that will 1) characterize different cellular populations in the tumor microenvironment (tumor-stromal cells, infiltrating myeloid cells, and infiltrating lymphocytes) through cell cytometry, targeted metabolomics through mass spectrometry, and transcriptomics RNA exome sequencing, as well as 2) provide tissue level characterization of UPS microenvironments through light sheet microscopy and multi-parametric magnetic resonance (spectroscopy, diffusion, and perfusion) imaging. In developing Dr. Neema Jamshidi's career path as a physician-scientist-engineering, this KL2 supplement application aligns well with his motivation to develop a research program that characterizes biological systems at multiple spatial hierarchies, 1) in order to select cases in which altered cellular phenotypes are manifested as organ level and systemic pathophysiological processes that can subsequently be used to 2) explore different strategies for manipulation of these phenotypes through targeted, precision therapies. The crux of the approach is to us quantitative measurements at multiple spatial scales (cross-sectional imaging at the tissue/organ level and high-throughput profiling at the cellular level) to construct predictive, data-driven models of biological processes. In the coming years he intends to pursue a systematic understanding of cell-cell interactions in oncologic diseases through integration of multi-omic profiling studies with clinical functional and cross sectional imaging studies (integrative radiogenomic/imaging genomic studies with constraint-based modeling) for Hierarchical Data-Driven Modeling of tumor microenvironments.